code: def fit(
      self,
      X,
      y,
      X_test=None,
      y_test=None,
      metric=None,
      feat_type=None,
      dataset_name=None,
  ):
      # TODO
      # regularly check https://github.com/scikit-learn/scikit-learn/issues/15336
      # whether histogram gradient boosting in scikit-learn finally support
      # sparse data
      is_sparse = scipy.sparse.issparse(X)
      if is_sparse:
          include_estimators = [
              "extra_trees",
              "passive_aggressive",
              "random_forest",
              "sgd",
              "mlp",
          ]
      else:
          include_estimators = [
              "extra_trees",
              "passive_aggressive",
              "random_forest",
              "sgd",
              "gradient_boosting",
              "mlp",
          ]
      self.include["classifier"] = include_estimators
  
      if self.metric is None:
          if len(y.shape) == 1 or y.shape[1] == 1:
              self.metric = accuracy
          else:
              self.metric = log_loss
  
      if self.metric in self.selector_metrics:
          metric_name = self.metric.name
          selector_file = self.selector_files[metric_name]
      else:
          metric_name = "balanced_accuracy"
          selector_file = self.selector_files[metric_name]
      with open(selector_file, "rb") as fh:
          selector = pickle.load(fh)
  
      metafeatures = pd.DataFrame(
          {dataset_name: [X.shape[1], X.shape[0]]}
      ).transpose()
      selection = np.argmax(selector.predict(metafeatures))
      automl_policy = self.strategies[selection]
  
      setting = {
          "RF_None_holdout_iterative_es_if": {
              "resampling_strategy": "holdout-iterative-fit",
              "fidelity": None,
          },
          "RF_None_3CV_iterative_es_if": {
              "resampling_strategy": "cv-iterative-fit",
              "folds": 3,
              "fidelity": None,
          },
          "RF_None_5CV_iterative_es_if": {
              "resampling_strategy": "cv-iterative-fit",
              "folds": 5,
              "fidelity": None,
          },
          "RF_None_10CV_iterative_es_if": {
              "resampling_strategy": "cv-iterative-fit",
              "folds": 10,
              "fidelity": None,
          },
          "RF_SH-eta4-i_holdout_iterative_es_if": {
              "resampling_strategy": "holdout-iterative-fit",
              "fidelity": "SH",
          },
          "RF_SH-eta4-i_3CV_iterative_es_if": {
              "resampling_strategy": "cv-iterative-fit",
              "folds": 3,
              "fidelity": "SH",
          },
          "RF_SH-eta4-i_5CV_iterative_es_if": {
              "resampling_strategy": "cv-iterative-fit",
              "folds": 5,
              "fidelity": "SH",
          },
          "RF_SH-eta4-i_10CV_iterative_es_if": {
              "resampling_strategy": "cv-iterative-fit",
              "folds": 10,
              "fidelity": "SH",
          },
      }[automl_policy]
  
      resampling_strategy = setting["resampling_strategy"]
      if resampling_strategy == "cv-iterative-fit":
          resampling_strategy_kwargs = {"folds": setting["folds"]}
      else:
          resampling_strategy_kwargs = None
  
      portfolio_file = (
          self.this_directory
          / metric_name
          / "askl2_portfolios"
          / ("%s.json" % automl_policy)
      )
      with open(portfolio_file) as fh:
          portfolio_json = json.load(fh)
      portfolio = portfolio_json["portfolio"]
  
      if setting["fidelity"] == "SH":
          smac_callback = SHObjectCallback(
              "iterations", 4, 5.0, portfolio
          )
      else:
          smac_callback = SmacObjectCallback(portfolio)
  
      self.resampling_strategy = resampling_strategy
      self.resampling_strategy_arguments = resampling_strategy_kwargs
      self.get_smac_object_callback = smac_callback
      return super().fit(
          X=X,
          y=y,
          X_test=X_test,
          y_test=y_test,
          feat_type=feat_type,
          dataset_name=dataset_name,
      )
  
imports: from __future__ import annotations
  from typing import Any, Dict, List, Mapping, Optional, Union
  import hashlib
  import json
  import os
  import pathlib
  import pickle
  import dask.distributed
  import numpy
  import pandas
  import scipy.sparse
  import sklearn
  from ConfigSpace import Configuration
  import autosklearn
  import autosklearn.experimental.selector
  from autosklearn.classification import AutoSklearnClassifier
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
  from autosklearn.ensembles.ensemble_selection import EnsembleSelection
  from autosklearn.metrics import Scorer, accuracy, balanced_accuracy, log_loss, roc_auc
label_desc: Use image node_id 3 for calling the AutoSklearn2Classifier obj's underlying member method code with example usage: obj.fit(X, y, X_test, y_test, metric, feat_type, dataset_name) and returns: super